Accurate forecasting is vital in the pharmaceutical industry due to the sector's complexity, dynamic nature, and high levels of investment and regulation. Traditional forecasting methods often fall short in managing the voluminous, variable, and velocity-driven data generated across the pharmaceutical value chain. Artificial Intelligence (AI), with its advanced data analytics and machine learning (ML) capabilities, has emerged as a transformative tool for enhancing forecasting accuracy, strategic planning, and decision-making in the pharma sector. This paper explores the integration of AI in pharmaceutical market forecasting, emphasizing its methodologies, advantages, current implementations, challenges, and future directions. A comprehensive review of AI applications in sales, demand, and R&D forecasting is provided, and a detailed analysis of AI models and their comparative performance is discussed. The study also presents a conceptual AI-driven forecasting model validated through case studies and simulation. This paper concludes by underscoring AI’s potential to revolutionize pharma forecasting, provided ethical, infrastructural, and regulatory issues are addressed.